{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/data/khuangaf/miniconda2/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['data/bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    datas = hf['inputs'].value\n",
    "    labels = hf['outputs'].value\n",
    "    input_times = hf['input_times'].value\n",
    "    output_times = hf['output_times'].value\n",
    "    original_inputs = hf['original_inputs'].value\n",
    "    original_outputs = hf['original_outputs'].value\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()\n",
    "#split training validation\n",
    "training_size = int(0.8* datas.shape[0])\n",
    "training_datas = datas[:training_size,:,:]\n",
    "training_labels = labels[:training_size,:,0]\n",
    "validation_datas = datas[training_size:,:,:]\n",
    "validation_labels = labels[training_size:,:,0]\n",
    "validation_original_outputs = original_outputs[training_size:,:,:]\n",
    "validation_original_inputs = original_inputs[training_size:,:,:]\n",
    "validation_input_times = input_times[training_size:,:,:]\n",
    "validation_output_times = output_times[training_size:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(validation_input_times,validation_output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "#Baseline 1\n",
    "predicted = validation_datas[:,-16:,0]\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "# we only care about the 0 axis, close price data\n",
    "\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "predicted_inverted.append(scaler.inverse_transform(predicted))\n",
    "print (np.array(predicted_inverted).shape)\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print (np.array(predicted_inverted).shape)\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "#Random Walk\n",
    "predicted = validation_datas[:,-16:,0]\n",
    "rand = np.random.normal(size=predicted.shape,loc=1.0, scale=0.005)\n",
    "predicted = np.multiply(predicted, rand)\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "# we only care about the 0 axis, close price data\n",
    "\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "predicted_inverted.append(scaler.inverse_transform(predicted))\n",
    "print (np.array(predicted_inverted).shape)\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print (np.array(predicted_inverted).shape)\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60112, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3536, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = validation_output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_df = prediction_df.loc[(prediction_df[\"times\"].dt.year == 2017 )&(prediction_df[\"times\"].dt.month > 7 ),: ]\n",
    "ground_true_df = ground_true_df.loc[(ground_true_df[\"times\"].dt.year == 2017 )&(ground_true_df[\"times\"].dt.month > 7 ),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NI4liy5bg4Z9Xb03jQKJrO8Nt7oRpanQVhLU1ugp0+Kc/06VjB6u8f/g0uZ7g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9DYELTv2AVX1zoiSZIkSbVgQklF7U4g9WiqvbtRdqnkUdETpeJJpVWrRsclwiUNyWlH7geM7DZY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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a737a62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "# plt.plot(prediction_df.times,prediction_df.value, label = 'Predicted')\n",
    "plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/bitcoin2015to2017_close_rw1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19122.460977938703"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
